DataViz Makeover 2

DataViz Makeover

Dive deeper into survey results of public’s willingness on Covid-19 vaccination.

Bai Xinyue
02-19-2021

Data Visualisation Link (Tableau Public) - https://public.tableau.com/profile/xinyue.bai#!/vizhome/ofStronglyAgree-gettingCOVID/DatavizMakeover2?publish=yes

1. Background Information

For this visualisation makeover, I have used data from Imperial College London YouGov Covid 19 Behaviour Tracker Data Hub. This data gathers global insights on people’s behaviours in response to COVID-19 covering 29 countries, in the form of survey questionnaire. In particular, this post is interested in exploring the willingness of the public on COVID-vaccination. In this blog, I will makeover visualisation on vaccine willingness done by one of the research scientists, by examining the following 3 survey questions in the context of different gender and employment status:
1. If a Covid-19 vaccine were made available to me this week, I would definitely get it.
2. I am worried about getting COVID19.
3. I am worried about potential side effects of a COVID19 vaccine.

2. Critiques and Suggestions

original visualisation

Figure 1: original visualisation

2.1 Clarity

SN Critique Suggestion
1 Visualisation on the left is intended to show “which country is more pro-vaccine”, by computing the percentage of each response and combining them into a percentage stacked bar chart. However, for each country, it’s not easy to see the proportion of pro-vaccine and compare it among different countries. Sort countries by proportion of pro-vaccine responses in descending order.
2 Moreover, scaling each bar into the same height is not clear enough to compare the difference between pro-vaccine responses and anti-vaccine responses. Place neutral responses at centre 0, negative value showing proportion of disagree responses and positive value showing proportion of agree responses.
3 It’s hard to distinguish bars having the same length. Add value label on each bar.
4 Legend is not very clear, i.e. what does 2, 3, 4 represent specifically? Change 2, 3, 4 to 2 – Agree, 3 – I don’t know, 4 – Disagree respectively.
5 Visualisation on the right is generally straightforward and clear. However, the simple percentage does not reflect statistical measures, how much you can expect your service results to reflect the view from the overall population. For example, if we have a high proportion of strongly agree responses with a wide margin of error, then this survey result is not very reliable. Calculate confidence interval for each value to get a more comprehensive view of the survey results.
6 Moreover, this visualisation is not convincing enough in a way that only high level view of the survey results is presented and insights from different angles are not revealed. For example, by examining deeper into gender level, males are generally more willing to get vaccinated than females respondents. Explore survey results from various perspectives, such as gender and employment status, and the relation between other survey questions.

2.2 Aesthetics

SN Critique Suggestion
1 It’s redundant and distractive to use five different colours for each response in the left visualisation, making it hard to view the survey results. | Use the same colour for the same group (1.agree 2.neutral 3. disagree) with different hue level. For example, dark green for strongly agree, light green for agree. Use the same colour for the same group (1.agree 2.neutral 3. disagree) with different hue level. For example, dark green for strongly agree, light green for agree.
2 The x-axis of two visualisations are not consistent, the first plot has no decimal place whereas the second plot has 2 decimal places. Make them consistent.
3 Generally good axis marks in twenties and grid lines to facilitate easy readings, clear use of fonts, font sizes and layout with very straightforward titles. Follow and format to ensure so.

3. Proposed Design

3.1 Sketch

Figure 2: sketch of proposed design

3.2 Advantages of Proposed Design

The first visualisation:
1. Clearly show which country is more pro-vaccine, by sorting rows according to % of pro-vaccine.
2. Easier to detect difference between two types of responses and difference between countries, with postive and negative x-axis representing pro-vaccine and anti-vaccine responses respectively.

The second visualisation:
- Clearly show how reliable the result is, by applying confidence interval.

The third visualisation:
- Help audiences better understand the public willingness on Covid-19 vaccination and the potential reasons why the public agrees or disagrees to getting vaccinated, by looking into the result of survey’s questions vac2.1(worry about getting COVID) and vac2.2(worry about potential side effect of vaccine), and plotting their relation wtih trend lines.

Other comments:
- By applying tooltip, the fourth and fifth visualisation provide audiences with a more comprehensive understanding of the survey results on vaccine willingness at the country level. Similarly, being able to filter via gender and employment status gives insights on the different behaviours within each group.

4. Data Visualisation Step-by-Step

4.1 Data Preparation

  1. Import australia.csv, canada.csv, denmark.csv, finland.csv, france.csv, germany.csv, italy.csv, japan.csv, netherlands.csv, norway.csv, singapore.csv, south-korea.csv, sweden.csv, united-kingdom.csv files into tableau.  
  2. Double click on New Union at the bottom left of Files and drag all csv files into Specific. Figure 3: union imported files
  3. Click on the triangle button on the top right and custom split variable vac_1 to obtain the score value for each response category (e.g. 1 - Strongly agree -> 1). Figure 4: select custom split Figure 5: custom split
  4. Edit aliases for new column created (i.e. vac_1 - Split 1), 1 -> 1 - Strongly, 2 -> 2 - Agree, 3 -> 3 - I don’t know, 4 -> 4 - Disagree, 5 -> 5 - Strongly disagree. Figure 6: custom split Figure 7: edit aliases before Figure 8: edit aliases after
  5. Rename new column as vac1_score. Figure 9: rename new column
  6. Click on “=Abc” symbol and change its data type to Number(whole). Figure 10: change column’s data type
  7. Rename Table Name as Country. Figure 11: rename table name

4.2 Creating Visualisation

4.2.1 Visualisation 1

  1. Open a new worksheet.  

  2. Click on Analysis -> Create Calculated Field.
    Figure 12: create a calculated field

  3. Create 7 calculated fields as follow:

    • Number of Records - Vac1
      Figure 13: calculate number of records that are not null
    • Total Records - Vac1
      Figure 14: calculate total records
    • Count Negative
      Figure 15: Count Negative
    • Total Count Negative
      Figure 16: calculate total count negative
    • Percentage - Vac1
      Figure 17: calculate percentage of records
    • Gantt Start
      Figure 18: Gantt Start
    • Gantt Percentage
      Figure 19: Gantt Percentage

  4. Drag Country to Rows, Grantt Percentage to Columns, vac1_score to Detail and Color
    Figure 20: after drag

  5. Gantt Percentage, click on the triangle button -> Compute Using -> vac1_score
    Figure 21: change gantt percentage’s compute using

  6. On the legend panel, right click on Null, exclude null records.
    Figure 22: exclude null

  7. On the Marks panel, right click on the triangle button of vac1_score (either color or detail), manually sort the vac1_score, in a order of strongly disagree -> disagree -> I don’t know -> agree -> strongly agree.

  1. Change chart type from Automatic to Gantt Bar
    Figure 28: change to gantt bar

  2. Drag Percentage - Vac1 to Size and Label.
    Figure 29: size and label

  3. Adjust label’s alignment.
    Figure 30: adjust label alignment

  4. Change label’s format from Automatic to Percentage.

  1. Similarly, right click on the x-axis and choose format, change axis’s value format to Percentage and add tick marks.
  1. Change color of the gantt bar. What it displays here is already the color I wanted. Red color represents anti-vaccine responses and green color represents pro-vaccine responses. Also, darker color implies more extreme responses (e.g. strongly agree and strongly disagree).
    Figure 36: change color of gantt bar
  2. Sort Country by % of pro-vaccine responses.
    Figure 37: sort gantt bar
  3. Right click on x-axis Edit Axis, change x-axis’s range to -100% to 100% and change its title to % of Total.
  1. Drag gender and employment_status to Filters.
  1. Add pro-vaccine and anti-vaccine annotation on the chart for better clarity.

4.2.2 Visualisation 2

  1. Open a new worksheet.
  2. Create 11 calculated fields as follow:
    • Number of Strongly Agree - Vac1
      Figure 47: Number of Strongly Agree - Vac1
    • Prop of strongly agree - vac1
      Figure 48: Prop of strongly agree - vac1
    • Z_95%
      Figure 49: Z_95%
    • Z_99%
      Figure 50: Z_99%
    • Prop-Standard Error
      Figure 51: Prop-SE
    • Prop_Margin of Error 95%
      Figure 52: Prop_Margin of Error 95%
    • Prop_Margin of Error 99%
      Figure 53: *Prop_Margin of Error 99%
    • Prop_Confidence Interval 95%
      Figure 54: Prop_Lower Limit 95%
      Figure 55: Prop_Upper Limit 95%
    • Prop_Confidence Interval 99%
      Figure 56: Prop_Lower Limit 99%
      Figure 57: Prop_Upper Limit 99%
  3. Drag Country to Rows and Prop of strongly agree - vac1 to Columns
  4. Drag Measure Values to the top of x-axis.   Figure 58: drag measure values
  5. In the Filters panel, click on Measure Names -> Edit Filter -> select 95% and 99% upper and lower limits.
  1. Change 95% and 99% confidence interval representation to Line, click on the path and choose the last Line Type.
  1. Drag Measure Names to path under Measure Values section.
    Figure 63: drag measure values to path
  2. Click on Measure Names (Detail) under Measure Values section and Prop of strongly agree - vac1 section, change it to Color.
  1. Change columns order, put Measure Values before Prop of strongly agree - vac1 and right click on the top x-axis -> synchronize axis.  
  1. Change the Measure Names’ order in the color legend, by adjusting in the Measure Values section.
  1. Hide the top x-axis, by unticking Show Header.
    Figure 72: hide header
  2. Sort rows according to % of strongly agree.
    Figure 73: sort data
  3. Change the color for 95% and 99% confidence interval according to your own preference, change axis’s number format and add tick marks. Detailed procedures are described in the visualisation 1.
  4. Final look of visualisation 2.   Figure 74: visualisation 2 final look

4.2.3 Visualisation 3

  1. Open a new worksheet.
  2. Create 8 calculated fields as follow:
    • Number of Records - Vac2.1
      Figure 75: Number of Records - Vac2.1
    • Number of Records - Vac2.2
      Figure 76: Number of Records - Vac2.2
    • Total Records - Vac2.1
      Figure 77: Total Records - Vac2.1
    • Total Records - Vac2.2
      Figure 78: Total Records - Vac2.2
    • Number of Strongly agree - Vac2.1
      Figure 79: Number of Strongly agree - Vac2.1
    • Number of Strongly agree - Vac2.2
      Figure 80: Number of Strongly agree - Vac2.2
    • Prop of strongly agree - vac2.1
      Figure 81: Prop of strongly agree - vac2.1
    • Prop of strongly agree - vac2.2
      Figure 82: Prop of strongly agree - vac2.2
  3. Drag Prop of strongly agree - vac1 to Columns, Prop of strongly agree - vac2.1 and Prop of strongly agree - vac2.2 to Rows, also drag Country to Detail under All section.
    Figure 83: after drag v3
  4. Drag Measure Names to Filters and only tick Prop of strongly agree - vac2.1 and Prop of strongly agree - vac2.2.
    Figure 84: mn to filter
  5. Drag Measure Names to Color under All section.
    Figure 85: add color
  6. Add two trendlines.
    Figure 86: add trendline
  7. Drag Country to Label and highlist points wiht minimum % of strongly agree 2.1/2.2 and maximum % of strongly agree 2.1/2.2.
  1. Change axis’s number format and add tick marks. Detailed procedures are described in the visualisation 1.
  2. Final look of visualisation 3.   Figure 89: final look v3